Master Thesis BOBOCEA Evelyn

ID number: 0948108

Name: Evelyn Bobocea

Master Thesis

Risk management using

derivatives in the European

Union

Supervisor:

Salvatore Miglietta

Hand-in date:

02.09.2013

Campus:

BI Oslo

Examination code and name:

GRA19003 – Thesis seminar in Finance

Programme:

Master of Science in Financial Economics

“This thesis is a part of the MSc programme at BI Norwegian Business School. The school takes no

responsibility for the methods used, results found and conclusions drawn."

Master Thesis in GRA19003 02.09.2013

Page i

Content

CONTENT ........................................................................................................................................ I

SUMMARY .................................................................................................................................... II

I. INTRODUCTION ..................................................................................................................... III

II. LITERATURE REVIEW ......................................................................................................... 1

III. DATA AND METHODOLOGY ............................................................................................. 4

III. 1. SAMPLE DESCRIPTION .......................................................................................................... 4

III. 2. HYPOTHESIS, VARIABLES AND THE ECONOMETRIC MODEL .................................................. 6

III. 3. RESULTS OF THE EMPIRICAL ANALYSIS ............................................................................. 10

IV. CONCLUSIONS ..................................................................................................................... 17

APPENDICES ............................................................................................................................... 18

REFERENCES .............................................................................................................................. 34

RESEARCH PAPERS ...................................................................................................................... 34

BOOKS ........................................................................................................................................ 35

ELECTRONIC RESOURCES ............................................................................................................ 35


Page ii

Summary

The aim of the present study is to analyse the effect of derivatives use on firm

value for a sample of 40 companies selected from the crude petroleum and gas

industrial sector, selected from countries members of the European Union, but

also from Norway, in order to build a comparison between the two. The idea of

this research has been strongly incentivised by the poor number of studies in risk

management regarding European companies, and mostly from the European

Union, but also by the comparison that could be built in relation to Norwegian

companies. The author’s results concluded that derivatives use do not have an

impact on firm value, regardless of economic context, while other variables

proved to be highly influential, such as size, capital expenditures ratio or return on

assets.


Page iii

I. INTRODUCTION

Risk management is a widely debated and discussed, but rather controversial issue

in the financial world. Hedging against risks has always been a matter of concern

for all companies and also a subject tackled by many studies in the extant

literature. The interest has been growing even stronger with the arousal of

derivative financial products, which have come to be appealing for companies,

individual investors or any other types of economic actors who were seeking to

reach higher profits and gains by reducing the exposure to risks as much as

possible.

However, some important lessons have been drawn back from the intensive use of

derivatives in the last decade and the catastrophic effects they have had on the

global economy that led to the 2008 financial crisis. Hence, one of the questions

of the present study is whether firms should make use of derivatives in times of

economic downturn and if this has a significant impact on the firm value.

The purpose of this paper is to analyse in a comparative way the oil industry of

the European Union and the one of Norway for several reasons. First of all, given

its position as one of the world’s most important economic pillars, it is interesting

to see in this industrial field how EU companies decide to manage risks, if they

make use of derivatives and what is the overall effect on profitability. Secondly, a

country rich in petroleum resources like Norway which is not part of any

international economic block has grown to be rich and the local companies are

prosperous and competitive. Finally, all these taken together, making a parallel

between the European Union and Norway would give a comparative view of risk

management practices under the use of derivatives in the oil industry.

A strong motivation of the author of the current study is to bring a contribution to

the existing literature, which does not treat so many studies related to risk

management and derivatives on European companies. What is more, European

Union companies are even less present in previous studies, which make the

incentive of this research even more imperative.


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The remainder of this paper is organized as follows: section II reviews previous

researches of the existing literature, section III describes the variables, the sample,

the econometric model and the empirical results, and section IV presents the final

conclusions.

.


Page 1

II. LITERATURE REVIEW

According to the classic Modigliani-Miller paradigm of perfect capital markets,

financial hedging is seen as irrelevant and with no economic sense, since there are

no information asymmetries, taxes or financial costs (Modigliani, 1958).

However, the rising importance of various risk categories in direct relation with

the well-functioning and value of firms, has made risk management become one

of the key-objectives of financial executives (Scharfestein, 1993). Beyond solely a

theoretical concept, risk management is treated in depth in various empirical

studies who seek to offer a useful framework to be implemented in business

practice.

There are several rationales for corporate hedging presented in the extant

literature.

One of them is introduced by Stulz (1984) in his study „Optimal hedging policies”

and refers to managerial motives, that is, the outgrowth of managers’ risk

aversion, as they are often large holders in the firm’s stock. Still, Stulz’s theory

has its weaknesses in the sense that it implicitly assumes the significant costs

beared by managers when trading in hedging contracts for their own account,

which is a direct reason for involving the firm in hedging activities.

Froot, Scharfestein and Stein (1993) expose another strong determinant for

hedging, given by taxes. They argue the relevance for optimal hedging through

the representation of taxes as a convex function of earnings, verified in practice by

firms for which the probability of negative earnings is quite high. In a similar

light, an additional factor they talk about is related to costs of financial distress

and debt capacity, motivating that the latter can be enhanced through hedging

practice. Last but not the least, capital market imperfections and inefficient

investment are shown to be another powerful argument for hedging, taking into

account that this way investment distortions associated with debt finance can be

reduced and thus, add value to the firm.


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Financial risk management has come forward in the last three decades due to the

increasing popularity of derivative financial products. Derivatives as a means of

hedging have been set forward in the 1980s and the 1990s mostly in relation to

managing foreign currency exposure as many firms began to develop growth

opportunities, but there is also a focus on interest rate exposure as well. It is worth

mentioning here that the impact of derivatives use on firm risk has also been

accounted for in several studies. In this direction, Guay (1999) analyzes how

firms’risk exposures are affected by derivatives use and he concludes that

generally the effect is on risk reduction and that accounting rules also play a key

role in this process.

For example, Stulz (1984) tackles with the problem of using forward contracts in

order to manage foreign currency exposure. In that sense, he derives a model

based on optimal hedging policies addressed to risk-averse agents and also puts a

focus on value-maximizing firms with regard to active hedging policies. His

findings conclude upon the fact that active hedging policies are indeed employed

by firms described above and that optimal hedging policies suppose choosing

positions whether in forward contracts or foreign bonds.

Another relevant research belongs to Geczy, Minton and Schrand (1997) who

investigate the use of currency derivatives so as to draw a clear line between

existing theories of hedging behavior. They show that both higher growth

opportunities and tighter financial constraints, but also consistent foreign-rate

exposure and economies of scale, are strong determinants for firms to use

currency derivatives, in order to reduce cash flow variation.

In the same fashion, Howton and Perfect (1998) examine derivatives use in

currency and interest rate exposures for both companies included in the S&P 500

index, but also for randomly selected ones. Their study reveals that the most used

instruments in interest-rate and foreign currency hedging, are swaps and forwards

and futures, respectively. What is more, according to their final remarks,

determinants of derivatives use vary accross the considered samples, but are

largely consistent with hedging theory.


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With regard to firm value maximization, the first to investigate the contribution of

derivatives to value enhancement have been Allayannis and Weston (2001). They

set their attention on a sample of non-financial firms and they reach the

conclusion that there is a positive relationship between firm value and hedging.

With respect to the oil industry, which is the area of interest of the present paper,

an important research has been conducted by Jin and Jorion (2006), in which they

examine the hedging activities of 119 U.S. oil and gas producers and their effect

on firm value, on a time span between 1998 and 2001. Their results state that on

the whole there is no significant difference in firm value between hedgers and

non-hedgers, as it would have been expected and contrary to Allayannis’s and

Weston’s findings.

Finally, a more recent study belongs to Lookman (2009), who examines and

quantifies the impact of hedging for oil and gas exploration and production firms.

What is new in his approach, compared to Jin and Jorion (2006) is that he stresses

upon the difference between primary and secondary risks, in an attempt to

emphasize the level of influence they have on the financial operation of the firm.

His findings point out that hedging the primary risk leads to a value discount,

whereas hedging the secondary risk generates a premium of higher value than the

value discount. He also concludes that hedging itself does not lead to a higher

value of the firm and that other variables which have not been taken into account

have an effect in this sense.


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III. DATA AND METHODOLOGY

III. 1. Sample description

The author has constructed the sample of the current research by using the

Compustat database1 in order to gather financial data (on which the variables of

the econometric model will be developed) concerning companies from the oil

industry and which are also located in countries from the European Union and

Norway. According to the Standard Industrial Classification2, the oil industry is

divided in four segments with their corresponding codes. That is crude petroleum

and natural gas (1311); drilling oil and gas wells (1381), oil and gas field

exploration services (1382) and oil and gas field services (1389).

However, given the relatively poor amount of data available, the sample has been

narrowed to the crude petroleum and gas sector. An explanation for this could be

the small dimensions of the European oil market. As found on Compustat

database, the oil industry in Europe comprises 698 companies, compared to the

2667 in North America, which translates into a European market roughly 25% of

the North-American one. Hence, the final sample of this paper contains 40

companies which operate in the crude petroleum and gas sector from United

Kingdom, Ireland, France, Germany and Sweden as references to the European

Union, and Norway as a separate entity. The aim of this separation is to build a

comparison between the European Union as global economic actor and Norway as

a single economic power and with rich petroleum resources.

What is also worth mentioning is that the companies are listed on London Stock

Exchange, Stockholm Stock Exchange, Oslo Stock Exchange and Frankfurt Stock

Exchange.

1

http://wrdsweb.wharton.upenn.edu/wrds/ds/comp/gfunda/index.cfm?navGroupHeader=Compustat

%20Monthly%20Updates&navGroup=Global

2 http://www.sec.gov/info/edgar/siccodes.htm


Page 5

The sample is then divided in consonance with two key time periods, 2005-2007

and 2008-2012, so as to analyse the effects of hedging on firm value in the recent

years that have offered a period of economic boom, but also a shattering global

financial crisis. The purpose of this division is to see whether there is a difference

in the incentives of companies to hedge or not to hedge with financial derivatives,

given the two important economic moments mentioned above. A company is

considered a hedger if it makes use of at least one derivative financial instrument

to hedge at least one category of risk (as stated in the annual financial reports).


Page 6

III. 2. Hypothesis, variables and the econometric model

The global hypothesis this paper puts forward is in accordance with a variety of

studies in the hedging literature that state that firms using derivatives for hedging

are rewarded with higher valuation by investors, which should translate into

higher firm value overall. Thus, the current study tests the influence of derivatives

use on firm value on the sample previously presented. All the quantitative analysis

is conducted in Microsoft Excel program and Eviews software.

Given that the focus of this study is on the oil industry, the author has gathered

two categories of data. First one comprises the variables that will be used to plot

the econometric model and the second aims to draw a view of the core risk factors

companies from this industry are generally exposed to but also taking in account

the information gathered from the annual reports of the companies on the time

span between 2005 and 2012.

As main variable of interest and also the centre of analysis of this paper is Tobin’s

Q, a proxy for firm value. Tobin’s Q is defined as the ratio of the sum between the

market value of equity and book value of total assets and the book value of equity.

This is in accordance with the study of Jin and Jorion (2006) and also the

algorithm of calculus undertaken my most of the researchers. The other variables

included in the analysis are divided in two wide categories: a hedging dummy

variable and a set of control variables (their formulas are presented in detail in

Appendix 2), that will be presented below.

Using a hedging dummy allows to control whether firms use derivatives of any

kind to hedge any type of risk they are being exposed to. This variable takes the

value of 1 if the firm uses derivatives for hedging and 0 if otherwise.

Control variables have the role of checking whether changes in the value of the

firm are due to other reasons that do not refer to hedging.


Page 7

In this sense, the control variables chosen are:

a) log of total assets as a proxy for firm size. Since there is not a clear

evidence on whether size leads to higher firm value, its sign

regarding Q is ambiguos,

b) a dividend dummy, which is used as a proxy for firms’access to

financial markets and takes the value of 1 if a firm paid dividends

in the year of interest and 0 otherwise,

c) ratio of long-term debt to book value of equity, an indication of

leverage, but also with an ambiguous sign,

d) return on assets, typically a measure of profitability which is

expected to be positively correlated with Q, as higher profitability

leads to higher firm value,

e) capital expenditures ratio¸ which is computed by scaling the total

capital expenses to sales and measures the investment growth and

should be positively related to Q,

f) research and development ratio, which is computed by dividind

the research and development expenses to total assets.

Basing itself on the empirical model developed by Allayannis and Weston

(2001), but also on a similar recent research made of Spyridon Kapitsinas on

Greek companies (2008), the econometric model of this current research has the

following shape:

( ) √

√

√

√

√

The explanation for choosing using square roots and radicals of third order for the

control variables instead of a linear regression relies on the similarity between the

graphical representations of the logarithm and the radical functions. Also, after

running a pile of tests and regressions in the software used, the author came to the

conclusion that the linear regression is not an adequate model for the current

research, as it leaded to no concluding result, and it turned to using radicals for

mathematical considerations.


Page 8

Since we are working with panel data, the methodology of calculus implies that

the variables put in the regression to be computed as averages of the annual values

that were gathered from Compustat.

For each time span, 2005-2007 and 2008-2012, there is a certain average that is

taken as value to be implemented in the model. An important detail to be pointed

out here is that due to unavailability of data concerning the market value of equity

on Compustat, the author has computed it using the returns of stocks and the

number of outstanding shares.

In order to compute the returns, daily prices have been collected for the time

period 2005-2012. The values of the returns have been calculated as a natural

logarithm of the ratio between the daily values of two consecutive trading days.

The computed daily stock returns have been average in order to determine a single

value for the 2005-2007 period and for 2008-2012 respectively.

Then, these amounts have been multiplied with the number of shares outstanding

so as to obtain the values of the market value of equity which is to be used in the

computation of Tobin’s Q. To mention as well that the number of outstanding

shares has been found on the Compustat database, and the daily prices of stocks

have been extracted from various sources, such as Yahoo! Finance3, Google

Finance4, Oslo Stock Exchange

5 website and Stockholm Stock Exchange

6

website.

Moving on to the second category of data, the author has gathered information

regarding the main sources of risks that the companies from the sample face.

Statistically speaking and in relation with the the companies’ annual financial

reports, interest rate risk, foreign-exchange rate risk and oil price risk are the

general exposures faced by the companies.

3 http://finance.yahoo.com/

4 http://www.google.com/finance

5 http://www.oslobors.no/

6 http://www.nasdaqomxnordic.com/


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As interest rates indicators, EURIBOR is assigned as interest rate for the

companies from the United Kingdom, Ireland, France and Germany, STIBOR for

Sweden and NIBOR for Norway. The data has been collected on a time span from

2005 to 2012, in annual represention, from the official sites of the central banks of

Norway7 and Sweden

8 and from the official website of EURIBOR

9 of the

European Union.

Concerning the exchange rates, the key exposures that the companies that operate

in United Kingdom and Ireland are to the GBP-USD rate, those from Germany

and France to the EUR-USD rate, and the SEK-USD and NOK-USD rates are

assigned to companies from Sweden and Norway respectively. The values of the

exchange rates have been collected from x-rates.com10

, in annual representation as

well, between 2005 and 2012.

As about the oil price risk, the indicator in this sense is the price of Europe Brent

crude oil, colected from U.S. Energy Administration website11

, in annual terms,

on a time period between 2005 and 2012.

Following the methodology of calculus, the interest rates, exchange rates and the

price of crude oil have been averaged in order to result in two key values for

2005-2007 and 2008-2012, respectively.

The summary statistics for both the variables analyzed in the model and the risk

factors are presented in tables 1 and 2 . Additionally, some robustness checks have

been performed to test the goodness of fit of the econometric model and the

results are encompassed in appendix 1.

7 http://www.norges-bank.no/en/price-stability/exchange-rates/

8 http://www.riksbank.se/en/Interest-and-exchange-rates

9 http://www.euribor-ebf.eu/euribor-org/euribor-rates.html

10 http://www.x-rates.com/historical/

11 http://www.eia.gov/dnav/pet/hist/LeafHandler.ashx?n=PET&s=RBRTE&f=A


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III. 3. Results of the empirical analysis

The outcome of the empirical analysis, as presented in the equation of the

econometric model, is in accordance with what Jin and Jorion found in their

research (2006), but not with Allayannis’s and Weston’s findings (2001), as the

overall result of the current paper is that derivatives use for hedging does not have

any influence on the firm value.

Table 1 presents summary statistics of the whole sample, with the two time spans

highlighted.

We can see that generally, companies have not been so incentivised to hedge

through derivatives during 2005-2007, as the mean value of the hedge dummy is 0

for this period. This is in connection with the companies’ risk management

policies which stated that the degree of exposure to various risk factors was not of

a consistent or critical level so as to determine them to employ derivatives. What

is more, there are companies who have reported that use of derivatives as means

of hedging is not at all included in their risk management practice.

A different view is given by the 2008-2012 period, which reflects an increased

drive for firms to turn to derivatives in their hedging practice and risk

management policy. This is shown by the mean value of the hedging dummy

variable of 0,5220. This is again consistent with the collected information from

the companies’ annual financial reports. Still, the discussion is more complex, as

some firms who did not choose to use derivatives to hedge against risks between

2005 and 2007 decide to turn to these instruments from 2008 to 2012, some who

used in the past, do not use them anymore and there are also firms who whether

used derivatives as means of hedging in all the two time spans or did not use at

all.


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It is worth pointing out that the companies from Norway have employed

derivatives in their risk management policy all along 2005 until 2012, according

to their financial reports and their most used instruments were interest rate swaps,

and forwards, which is quite the same in the case of the companies from the rest

of the sample

Table 2 summarizes the key statistics for the risk factors taken into consideration

for the current paper.

For the first time span analysed, between 2005 and 2007, we can see that interest

rates vary from a minimum value of 3,11% to a maximum of 3,87%, with a mean

value of 3,41%. Exchange rates know the highest volatility of all the three risk

categories, with a standard deviation of 32,27% and the values move between a

minimum of 0,1426 and a maximum of 1,3215.

As for 2008-2012, there is a strong arousal in the oil price, reaching a value of

92,23$/barrel, compared to 64,05$/barrel in the previous time span. There is also a

general increasing in volatility for all the risk factors, but interest rates however

know a downturn in value, ranging from 2,18% to 3,74%. Exchange rates are

facing a growth in value as well, but the interval range is not significantly

different from the one registered in 2005-2007, with a minimum value of 0,1414

and a maximum of 1,3470.

Tables 3 and 4 express the regression outputs of the sample analysis, for each time

period. Corely speaking, the coefficient of the hedging dummy variable proved

not to be significant for neither of the two time periods considered, which

emphasizes the fact that the use of derivatives does not have an impact on firm

value. The dividend dummy proved not to be relevant in drawing any conclusion

on the influence on firm value, as all the companies from the sample did not pay

any dividends during the whole time period from 2005 to 2012.


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What is worth mentioning is that the statistcally significant variable for the 2005-

2007 period is the return on assets, which means that profitability was high

enough to determine a strong influence on the value of the firm and the sign of the

coefficient is positive, which means a positive association with Q.

As for 2008-2012, capital expenditures ratio and size are the significant variables,

which means that growth opportunities and company dimension have contributed

to changes in the firm value. The capital expenditures ratio is possitively

associated with Q, due to the positive sign of the coefficient, while size is

negatively associated. The explanation would therefore be that growth

opportunities have enhanced firm value, while big size means lowered firm value

and analogous for small size.


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Table 1: Summary statistics for the full sample

The table presents the summary statistics for all the variables included in the OLS

regression. The variables are: Tobin Q, Hedging dummy-a dummy variable that

equals 1 if firms use derivatives in the analyzed period and 0 otherwise, Debt

ratio, ROA-Return on Assets, Capital expenditures ratio, Size, R&D- ratio-

research& development ratio.

Table 1 presents descriptive statistics for the all of the variables included in the

OLS regression. There has been a slight decrease in the debt ratio from 37.62% to

35.55%, and also for ROA from -14.13% to -18.58%. It can be also noted that

companies recorded an in increase in capital expenditures, and a decrease of 4%

in the research and development expenditures. Furthermore, while in the first

period 2005-2007, the mean firm size was 0. 6737, in 2008-2009 there has been

an increase up to 0.9093.

No. of obs. Mean Median Std.dev. Min. Max. Skewness Kurtosis

Ln of Q 40 4.5956 4.9131 3.0266 -0.1235 13.2999 0.3941 0.3270

Hedging

dummy40

0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

Debt ratio 40 0.3762 0.2124 0.4021 0.0000 1.2188 0.4585 -1.3023

ROA 40 -0.1413 -0.3046 0.4765 -1.0222 0.6582 0.2743 -1.1543

Capital

expenditures

ratio

40

1.2114 0.8467 1.2704 0.0000 6.6104 2.9827 10.1554

Size 40 0.6737 0.9184 1.0715 -1.4071 2.0553 -0.4167 -1.1231

R&D ratio 40 0.0076 0.0000 0.0281 0.0000 0.1620 4.7207 24.5408

Ln of Q 40 5188.2869 46.5847 20639.0334 1.0000 105307.9064 4.3856 18.6602

Hedging

dummy40

0.5250 1.0000 0.5057 0.0000 1.0000 -0.1041 -2.0967

Debt ratio 40 0.3555 0.3712 0.3594 0.0000 1.1846 0.4172 -1.1433

ROA 40 -0.1858 -0.2920 0.4913 -1.4732 0.6022 -0.2050 -0.4792

Capital

expenditures

ratio

40

1.5192 0.8611 3.2411 0.0000 20.9752 5.8244 35.6055

Size 40 0.9093 1.3384 1.0825 -1.6662 1.9511 -1.2259 0.4289

R&D ratio 40 0.0072 0.0000 0.0311 0.0000 0.1800 5.0160 26.4291

PANEL B 2008-2012

PANEL A 2005-2007


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Table 2: Summary statistics for risk factors

This table presents the summary statistics for the stock returns, oil price, interest

rates, and exchange rates returns. The table depicts the descriptive statistics for

both of the periods analyzed.

Table 2 reports the summary statistics for the risk factors in the two analyzed

periods. There is an increase in the average return for two of the risk factors

analyzed. For the other two, stock price return and interest rate, the mean return

has decreased from -0.0002 to -0.0021, respectively from 0.0341 to 0.0233. The

same evolution can also be noted for the median value.

No. of obs. Mean Median Std.dev. Min. Max. Skewness Kurtosis

Stock price

return 40 -0.0002 0.0005 0.0023 -0.0094 0.0026 -2.0638 5.9165

Oil price 40 64.0567 64.0567 0.0000 64.0567 64.0567 0.0000 0.0000

Interest

rates 40 0.0341 0.0340 0.0015 0.0311 0.0387 1.4696 5.2717

Exchange

rates 40 0.9717 1.0824 0.3227 0.1426 1.3215 -2.1395 3.1569

Stock price

return 40 -0.0021 -0.0005 0.0087 -0.0554 0.0022 -6.1562 38.5492

Oil price 40 92.2360 92.2360 0.0000 92.2360 92.2360 1.0394 -2.1081

Interest

rates 40 0.0233 0.0218 0.0042 0.0218 0.0374 3.0110 8.0137

Exchange

rates 40 1.0835 1.2118 0.3613 0.1414 1.3470 -2.2802 3.4575

PANEL A 2005-2007

PANEL B 2008-2012


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Table 3: Regression output for the 2005-2007 period

This table presents the coefficients, standard errors, t-statistics, p-values from the

regression output for the period 2005-2007. The model used in a multifactor OLS

regression. The dependent variable is Tobin Q. The independent variables

include: Hedging dummy-a dummy variable that equals 1 if firms use derivatives

in the analyzed period and 0 otherwise, Debt ratio, ROA-Return on Assets,

Capital expenditures ratio, Size, R&D- ratio-research& development ratio. The

regression output also reports the R Square value.

Regression Statistics

Multiple R 0.7238

R Square 0.5238

Adjusted R Square 0.4373

Standard Error 2.2704

Observations 40

Coefficients

Standard

Error t Stat

P-

value

Lower

95%

Upper

95%

Lower

95%

Upper

95%

Intercept 4.5356 0.7552 6.0060 0.0000 2.9992 6.0720 2.9992 6.0720

Hedging

dummy 0.8334 0.9122 0.9136 0.3676 -1.0226 2.6893 -1.0226 2.6893

Debt

ratio -1.5718 1.1369 -1.3826 0.1761 -3.8849 0.7412 -3.8849 0.7412

ROA -3.1329 1.0298 -3.0422 0.0046 -5.2281 -1.0377 -5.2281 -1.0377

Capital

expenditures

ratio 0.2230 0.3273 0.6813 0.5005 -0.4429 0.8889 -0.4429 0.8889

Size -0.5868 0.5429 -1.0809 0.2876 -1.6913 0.5177 -1.6913 0.5177

R&D

ratio 11.0868 13.2091 0.8393 0.4073 -15.7874 37.9610 -15.7874 37.9610


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Table 4: Regression output for the 2008-2012 period

This table presents the coefficients, standard errors, t-statistics, p-values from the

regression output for the period 2008-2012. The model used in a multifactor OLS

regression. The dependent variable is Tobin Q. The independent variables

include: Hedging dummy-a dummy variable that equals 1 if firms use derivatives

in the analyzed period and 0 otherwise, Debt ratio, ROA-Return on Assets,

Capital expenditures ratio, Size, R&D- ratio-research& development ratio. The

regression output also reports the R Square value.

Multiple R 0.6054

R Square 0.3665

Adjusted R Square 0.2513

Standard Error 17858.3340

Observations 40

Coefficients

Standard

Error t Stat

P-

value Lower 95%

Upper

95% Lower 95%

Upper

95%

Intercept 26068.4743 7763.4361 3.3579 0.0020 10273.64486 41863.3037 10273.6449 41863.3037

Hedging

dummy -1488.3300 6591.3634 -0.2258 0.8227 -14898.55963 11921.8996 -14898.5596 11921.8996

Debt

ratio 9712.5879 10249.8401 0.9476 0.3502 -11140.86870 30566.0444 -11140.8687 30566.0444

ROA 11949.7658 9278.2056 1.2879 0.2067 -6926.88549 30826.4171 -6926.8855 30826.4171

Capital

expenditures

ratio -2206.2499 1030.6937 -2.1405 0.0398 -4303.21206 -109.2877 -4303.2121 -109.2877

Size -18493.3681 5386.5839 -3.4332 0.0016 -29452.45551 -7534.2807 -29452.4555 -7534.2807

R&D

ratio -162554.5179 109182.3836 -1.4888 0.1460 -384687.74754 59578.7118 -384687.7475 59578.7118

Regression Statistics


Page 17

IV. CONCLUSIONS

The present study has shown that even in the presence of exposure to risk,

companies who hedge do not differ from some who would choose not to do it, in

terms of firm value.

We were able to observe that the majority of the companies included in the

sample did not show incentives to hedge with derivatives during the 2005-2007,

while a significant number of non-hedgers decided to become hedgers in the next

time period from 2008 to 2012. This behavior could be characterized in terms of

fear of worse outcomes at the brutal contact with the financial crisis. However, we

can not omit the companies who decided to stick on their risk management policy

principles and not include derivatives in their hedging practice. This can be an

outspeaking proof of strong corporate governance that does not allow flows so as

to take into consideration the use of derivatives as means of hedging.

A conservative behavior could be attributed to the companies from Norway as

well. Given that the risk management practice suffered no changes regarding

hedging before or during the financial crisis, this could be motivated by the fact

that Norway is a closed economy and the degree of exposure to risk factors is not

as strong as for the companies in the European Union.

Companies from Norway could be given as example of prosperity using

derivatives in crisis and in good economic turns, but the picture could be a bit

biased due to the fact that this country is not a player on the global economy

scene, so the benefits of the firms might be available only in a local context.

Given the small dimensions of the European Union oil and derivatives markets,

compared to North America, the economic results of the studied companies are

quite impressive, as they are not affected neither by hedging, nor by not hedging.

Finally, the author is engaged in further research on this topic, so as to improve

the econometric model and the methods used for the quantitative analysis, so as to

attain maximum of accuracy on results and additional information.


Page 18

Appendices

Appendix 1

Table 1 Results of Augmented Dickey Fuller Unit Root Test

This table summarizes the results of Augmented Dickey Fuller Test. The null

hypothesis of this test is that series have unit root. In order to reject or accept this

hypothesis, the absolute values of the numbers presented above are compared with

the critical values: 1% - 3.6155, 5% - 2.9411, 10% -2.6090. If the numbers of the

coefficients presented above are higher in absolute values than the critical values,

then the null hypothesis is rejected. As it can be seen, all series do not suffer from

unit root bias.

Augmented Dickey Fuller Test Regression 1 Regression 2

TOBIN Q -4.9734 -4.5350

CAP EXPENDITURE -5.3131 -3.9757

DEBT RATIO -5.3157 -5.9851

HEDGING -5.9555 -5.6627

R&D RATIO -6.5680 -5.7171

ROA -4.2460 -4.3306

SIZE -5.2454 -5.0245


Page 19

Table 2 Results of Jarque Bera Test

This table presents the results of Jarque Bera test. In order to accept the null

hypothesis which assumes that the residual series are normally distributed, JB

statistic has to be lower than the critical values presented above. As it can be

noticed, the residuals are normally distributed so the coefficients of the estimators

are reliable.

Table 3 Results of Variance Inflation Factors Test

This table shows the results of the test I applied in order to test for

multicollinearity. I used the Centered Variance Inflation Factors and the cutoff

level of 5. As it can be seen all the values for both regressions are below 5 which

means that the series are not correlated. Therefore, the regressions are not affected

by multicollinearity.

Jarque Bera Test Regression 1 Regression 2

JB Stat. 9.5819 0.0309

Degrees of freedom 6 6

Critical values

1% level 16.8120 16.8120

5% level 12.5920 12.5920

10% level 10.6450 10.6450

Probability 0.0083 0.9846

Variable Regression 1 Regression 2

CAP EXPENDITURE 1.3045 1.3589

DEBT RATIO 1.5571 1.6593

HEDGING 1.2536 2.5412

R&D RATIO 1.0792 1.3646

ROA 1.7534 4.1582

SIZE 2.5847 1.4058


Page 20

Table 4 Results of White Test

This table shows the results of White test. I used this test in order to test the errors

series for heteroskedasticity. If the errors are not homoscedastic then the p-values

of the estimators are altered. As it can be seen above, the probability of F-statistics

is higher than the standard levels of 1%, 5% or 10%, which means that the null

hypothesis of no heteroskedasticity is rejected. The errors are homoscedastic and

the estimators are reliable.

Table 5 Results of Durbin Watson Test

This table shows the results of the Durbin Watson test, which I used to test for

autocorrelation. K – the number of estimators, N – the number of observations, D

lower and D upper are extracted based on K and N. As it can be seen above both

regressions are positive correlated.

White Test Regression 1 Regression 2

F-statistic 0.6057 0.2698

Prob. F 0.8672 0.9977

Durbin Watson Test 5%

level of significanceRegression (1) Regression (2)

K 6 6

N 40 40

DL 1.2305 1.2305

Du 1.7859 1.7859

DB Stat 1.6085 1.5736

DB Stat < DL No Positive Correl No Positive Correl

DB Stat > Du Positive Correl Positive Correl

Du< DB Stat < DL Inconclusive Inconclusive

(4 - DB Stat) < DL No Negative Correl No Negative Correl

(4 - DB Stat) > Du No Negative Correl No Negative Correl

Du < (4 - DB Stat) < DL No Negative Correl No Negative Correl


Page 21

Appendix 2 – Formulas

)

)

) (total assets)

)

) (

)

6)

)

)


Page 22

Appendix 3 – Preliminary thesis report

BI Norwegian Business School

MSc Financial Economics

Preliminary thesis report:

Evaluating risk management in the oil

industry using derivatives

Date of submission:

15.01.2013

Supervisor:

Salvatore Miglietta

Written by:

Evelyn Bobocea

ID: 0948108

– Oslo –


Page 23

Table of Contents

ABSTRACT ................................................................................................................................... 24

INTRODUCTION......................................................................................................................... 25

LITERATURE REVIEW............................................................................................................. 26

DATA AND METHODOLOGY .................................................................................................. 31

REFERENCES .............................................................................................................................. 32


Page 24

ABSTRACT

The purpose of this paper is to analyze different risk management approaches

under the use of derivatives for a number of 73 companies from the petroleum

industry selected from relevant countries in America and Europe.

The main goal of this study is to determine the best risk management alternatives

to be put in practices by firms in relation to the price of crude oil, the interest rate

and the inflation rate for a time pattern chosen.


Page 25

INTRODUCTION

The petroleum industry is amongst the most volatile ones, therefore companies

operating in this sector need to give particular attention to the risks they are being

exposed to, so as to find the best hedging methods that will minimize their losses.

Given the global financial crisis that started in 2007 and still resides in the general

economic context in the present days, it is even of greater importance to have an

attentive look at one of the most prominent industry fields in the world and how

risk management practices should work.

In the present study, the focus targets risk concerning price, interest rates and

inflation rate, as these are the main factors which affect the winnings of

companies operating in the petroleum industry.

Price risk is the most important risk factor which could affect the winnings of an

oil-operating company as there is a strong correlation between the evolution of

crude oil price and the price of stocks related to the companies in discussion.

Secondly, interest rate risk is another key-element in a risk management analysis

because the various moves it suffers during a certain time period is reflected in the

evolution of a company’s profits.

Last but not least, the inflation rate is another indicator which we have chosen to

analyze through the lens of risk because an industry like the petroleum one is very

sensitive to the ups and downs of the inflation rate, therefore it is more than

necessary to take it into account when analyzing risk for an oil-operating

company.

Looking at these three risk elements, we should be able to decide which type of

derivatives are best to be used so as to minimize the risk exposure for the firm.

Nevertheless, besides this quantitative analysis, it is also important to consider

several other factors such as corporate governance issues, cultural dimensions and

the political and economic contexts which are determinant for our overall

conclusions of the study.


Page 26

LITERATURE REVIEW

The literature of speciality has tackled many key issues concerning risk

management practices and analysis.

The literature on oil price impacts can be broadly divided into macroeconomic

impacts and impacts on stock market returns in general and market return of oil

and gas firms. The debate on the macroeconomic impacts of oil price shocks

began in the mid-1970s.

The earlier studies regressed GDP on oil prices (Rasche & Tatom 1997a, 1997b).

In fact it was Hamilton's, 1983 work indicating that oil price increases have

reduced US output growth between 1948 and 1980, which sparked debate on this

topic.

Hamilton (1983) showed that an oil price hike preceded all but one recession (in

1960) in US since second World War.

Gisser and Goodwin (1986) corroborated the results of Hamilton (1983). Studies

by Bohi (1989) and Mork (1989) focused on the asymmetric impacts of the oil

price change; the argument being that oil price movements up and down have

opposite effects on the production possibility curve of firms, causing changes in

resource allocation.

Hamilton's (2000) updated study for the period 1948–2000 showed that GDP

decreased by 1.4% in the fourth quarter after the initial price shock. However, the

study by Rotenberg and Woodford (1996) for the same period as Hamilton (2000)

showed a much larger output loss (2.5% of GDP).

Empirical studies have also examined the relationship between oil price changes

and stock prices at the (a) firm and (b) aggregate level. At the firm level, one of

the earlier studies on the relationship between oil prices and returns of oil firms

was that of Al-Mudhaf and Goodwin (1993). They used a multi-factor of the

arbitrage pricing theory (APT) model to analyze and explain the difference in the


Page 27

market and oil price returns in 29 US oil companies (listed in New York Stock

Exchange) in a period surrounding the oil shock of 1973.

They found that oil price shocks drove up returns for oil firms. Rajagopal and

Venkatachalam (2000) studied 25 petroleum refining companies and concluded

that earnings of these firms exhibited a strong correlation with the firms' oil betas

(in the range of 0.55 to 0.66).

A few studies concentrate on the impact of oil prices on stock market returns at

the industry level. Lee and Ni (2002) find that for industries that are oil-intensive

in production, such as petroleum refinery and industrial chemicals, the

predominate impact of oil shocks is on the cost-side, while for other industries,

such as the automobile industry, the main effect of oil price shocks is on the

demand-side. Gogineni (2010) provides empirical evidence that oil price changes

affect stock returns of industries on the cost-side and demand-side.

Park and Ratti (2008) show that Norway as an oil exporter shows a statistically

significant positive relationship between stock market returns and oil prices.

Hammoudeh and Li (2005) based on daily data for the period 1986–2003, on the

relationship between oil prices and the return in the stock markets in oil-based

countries (Mexico and Norway) and two major oil-sensitive industries (US oil and

transportation industries), found that oil price growth leads stock returns of oil-

exporting countries. But they also found a negative association with the world

stock market index (MSCI World Index) and returns of the US transportation

industry and oil prices. Boyer and Filion (2007) using the multifactor framework

to analyze the determinants of Canadian oil and gas stock companies, reveal a

significant relationship between oil price changes and stock returns.

Park and Ratti (2008) found that the impact of oil prices on the variability of stock

returns is greater than that of interest rates. A rise in oil prices is associated with a

significant increase in the short-term interest rates in the U.S. and most European

countries. Similarly, Sadorsky's (2001) study of Canadian oil and gas companies

for the period of 1983:04 to 1999:04 found that crude oil prices, exchange rate and

interest rates have a large and significant impact on stock prices. Sadorsky's study

finds a significant positive relationship between the oil and gas equity index and


Page 28

the price of crude oil, with a 1% change in oil prices being associated with a

change of 0.03% in the value of the oil and gas equity index. Papapetrou's (2001)

study using impulse response functions found that oil prices are an important

factor in explaining stock price movements in Greece.

The study by Cormier and Magnan (2002) found evidence of income smoothing

among Canadian oil and gas companies. They found that there is a close

relationship between stock market evaluation and future cash flows. Byard,

Hossain, and Mitra (2007) examined earnings management of U.S. oil and gas

firms after the devastating impact of hurricanes Katrina and Rita in 2005. Oil

prices peaked after the effects of both hurricanes. Normally, large rises in oil

prices trigger politicians and regulators that wealth is transferred to oil and gas

firms at the expense of consumers (Watts & Zimmerman 1986). According to the

political cost hypothesis, managers have a strong incentive to engage in earnings

management designed to lower reported earnings during periods of severe

political scrutiny. Byard et al. (2007) find significant abnormal income-decreasing

accruals immediately after the impact of hurricanes Katrina and Rita.

On the contrary, studies by Chen et al. (1986) argue that there is no special reward

for oil price risk in the stock market. Huang et al.'s (1996) study found a

significant relationship between daily oil futures returns and daily US stock

returns. They found that oil future returns had no impact on the broad based

market index such as the S&P 500.

Malliaris and Urrutia (1995) provide evidence of share prices reacting negatively

to the Persian Gulf crises. Studies on the impact of oil prices on the stock markets

of various countries have also revealed interesting results. The study by Jones and

Kaul (1996) (for the period 1947–1991) on the impact of oil prices on expected

returns of oil firms in Canada, Japan, the United Kingdom and the USA. Using a

standard cash flow dividend valuation model they test whether the reaction of

international stock markets to oil shocks can be justified by current and future

changes in real cash flows. They concluded that the reaction of Canadian and US

stock prices to oil prices can be completely accounted for by the impact of these

shocks on real cash flows. However, they did not find a strongrelationship for


Page 29

similar firms in Japan and United Kingdom. Similarly, the study by Faff and

Brailsford (1999) reports a significant positive impact of oil price change on oil

and gas and diversified industries in Australia. On the other hand, oil-dependent

industries (where oil is an input) like paper and packaging, transport were found

to be negatively impacted. Kilian and Park's (2009) study, based on monthly data

for the period of 1973 to 2006 found that demand and supply shocks driving the

global crude oil market jointly account for 22% of the long-run variations in U.S

real stock returns. A similar study by Bhar and Nikolova (2010) for Russia found

that global oil price returns have a significant impact on Russian equity returns

and volatility. El-Sharif, Brown, Burton, Nixon, and Russell (2005) examined the

influence of the price of crude oil on equity values in the oil and gas sector using

data relating to the United Kingdom, the largest oil producer in the European

Union. Their evidence indicates that the relationship is always positive, often

highly significant and reflects the direct impact of volatility in the price of crude

oil on share values within the sector. In addition, their empirical results indicate

that for non-oil and gas industries the effect of oil price volatility on equity returns

is minimal. These results confirm that industries are not homogeneous and that

different variables can impact industry returns in various ways (Faff & Brailsford

1999).

There is a related strand of literature that considers corporate governance as

important determinants of firm performance. Studies by Morck, Shleifer, and

Vishny (1988), McConnell and Serveas (1990) and Thomsen and Pedersen (2000)

investigated the relationship between ownership concentration and firm

performance. They show that there is a nonlinear relationship between ownership

concentration and firm performance beyond a certain point, which indicates

entrenchment of incumbent management or large shareholders.

Gompers, Ishii, and Metrick (2003) find that firms with strong shareholder rights

outperform, on a risk-adjusted basis, firms with weak shareholder rights. This

result indicates that that good governance has a positive impact on corporate

performance. Bhagat and Bolton (2008) find that better governance, stock

ownership of board members, and CEO-Chair separation is significantly


Page 30

positively correlated with operating performance. Wolf (2009) uses a

comprehensive dataset of oil and gas companies, covering both privately and

publicly owned firms over the period 1987–2006, to examine whether or not

ownership matters in economic terms.

The study supports the hypothesis that private ownership encourages better

performance and greater efficiency than state ownership does. A review of the

aforementioned studies showed that crude oil prices have a positive impact on the

financial performance of oil and gas companies at the firm level while has a

negative impact on oil-using (where oil is used as an input) firms.

At the aggregate (overall stock market) level, the evidence is mixed: in oil stock

dominated stocks markets like Canada and the USA, the impact of an oil price

change is generally positive, but it is weak in non-commodity based stock markets

like Japan and the United Kingdom. It seems plausible that commodity prices are

to some extent reflected in the share price, but less of an effect on the accounting

profit measures.


Page 31

DATA AND METHODOLOGY

We have selected daily closing values for the stock prices of 73 companies from

America and Europe, that is 25 companies from the Americas and 48 from Europe

respectively, for a time span ranging from January 1st 2000 to December 31st

2012.

The time span has been chosen so that it encompasses the period before the

financial crisis in 2007 has started, the period in which the crisis has been most

prominent – 2007-2009 and post-crisis from 2010 to 2012. It is important to

analyze each of these periods in order to be able to draw up conclusions that could

also apply as a general case when the economy offers a boom followed by a

considerable depression.

Also, we have selected the daily values for the price of crude oil WTI, the interest

rates for the countries considered and the inflation rate for the same time horizon.

The idea of this analysis is to check whether there is a connection between the

three economic factors just above and the evolution of the companies’ winnings.

For this we will employ a set of regressions to see the influence that each of the

risk factors has on each of the companies and how strong this is. In order to do so,

we will set up as the dependent variable the companies’ stocks prices, which we

will analyze in connection with the price of crude oil WTI, interest rates and

inflation rate as independent variables.

In addition to this, it is also important to check whether the variables in our

analysis are cointegrated. For this, we will employ a series of Granger tests in the

Eviews program.


Page 32

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Master Thesis BOBOCEA Evelyn